Plant roots grow within complex microbial communities, forming interactions with the root and with each other, ranging from pathogenesis to mutuality [1]. These communities found either on (rhizoplane) or within the root's endophytic compartments (EC) or in close proximity to the root surface (rhizosphere), hold a vast genomic functional trait reservoir that may be harnessed for improving crop performance. Indeed, a large number of bacterial strains isolated from plant roots can positively affect plant phenotypes such as shoot size, germination rate or pathogen resistance [2, 3]. However, due to the complexity of natural microbial communities, and the prevalence of metabolic exchange in these communities, such single strains are nearly always ineffective when applied as probiotics to plants growing in heterologous, standing microbial communities. Inoculating plants with bacterial consortia that either capture the functional range within a taxon, or provide overlapping function from diverse taxa, has a higher potential of consistently affecting plant performance and persistence under controlled conditions and, ultimately, in field settings. As designing and testing microbial consortia is exponentially more complex than testing single isolates for a given phenotype, there is a need for formulating and testing design principles that will assist in constructing such communities. This proposal aims to devise and test methods to design beneficial microbial consortia by optimizing mutually beneficial microbe-microbe interactions. This will be achieved by integrating genomic and metabolomic information to design consortia that are predicted to maximize mutually beneficial and minimize antagonistic interactions. These predictions will be tested in mesocosm plant colonization experiments. The design of microbial consortia will be guided in parallel by two main principles: (a) the hypothesis that plant and bacterial performance correlate with the level of bacterial diversity and (b) that the level of metabolic complementarity within the plant microbiome is predictive of the level of mutually beneficial interactions, and thu, of microbiome productivity. The productivity of a beneficial plant microbiome, should, in turn, increase plant productivity. In order to test these hypotheses, a diverse library of 200 genome-sequenced bacterial strains isolated from Arabidopsis thaliana roots will be used. Bacterial consortia will be constructed in a way that maximizes the ranges of genomic diversity and metabolic complementarity, as defined below. An array of gnotobiotic A. thaliana will be inoculated with these consortia and plant growth, germination, flowering time, seed yield, resistance to fungal infection and transcriptional profiles will be measured. Linking genome-derived predictions of community function to measurable phenotypes will help us infer design principles that will be applied first in controlled settings and ultimately in field settings.